Know if you should bid, before you spend a day finding out.
Government tender documents run 100–200+ pages, dense with eligibility criteria, financial thresholds, certification requirements, and legal clauses buried across dozens of sections. Before a contractor or MSME can even decide whether to bid, someone has to manually read the entire document, cross-check it against the business's own turnover, experience, and certifications, and hope nothing important got missed.
This forces small businesses into an expensive choice: spend days on manual review, or pay a bid consultancy just to find out if they even qualify — often only to discover, too late, that they don't.
| 👥 Who this affects | Contractors, MSMEs, and bid consultancy firms participating in government tender bidding (GeM and similar procurement platforms) |
| ⏱️ Why it matters | Every hour spent manually reviewing a tender you're not eligible for is an hour — and often money — a small business can't get back |
TenderIQ lets a business upload a tender PDF and their own business profile, and get back a clear, explainable eligibility verdict in minutes — not days.
- Upload — the user uploads a tender PDF and selects (or creates) their business profile (turnover, experience, certifications, MSME/Udyam category).
- AI reads the document — Google Gemini processes the tender section by section, extracting turnover requirements, experience thresholds, certifications, EMD amount, submission deadlines, required documents, and risky contract clauses — each one tied back to the exact page it came from.
- A deterministic rule engine decides — critically, the AI never decides eligibility. Every extracted value is run through a versioned, transparent rules pack that checks the business's actual profile against the tender's actual requirements — the same way a human reviewer would, just instantly and consistently.
- The verdict comes with its reasoning — not just Eligible / Not Eligible, but why: which specific criteria passed or failed, what the gap is, whether an MSME exemption applied, and a confidence score reflecting how much of the document could be reliably read.
This AI-extracts, rules-decide split is deliberate. It means every verdict is explainable and reproducible, not a black-box judgment call from a language model.
Why this is effective: it combines what AI is genuinely good at (reading and structuring long, messy documents) with what a rule engine is good at (consistent, auditable, deterministic decisions) — giving small businesses a fast, trustworthy first read on a tender, with the receipts to back it up.
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📊 Tender Match Score A single, weighted score (0–100%) combining eligibility pass rate, business readiness, extraction confidence, and contract risk 🎯 Explainable Verdicts Eligible, Eligible with Conditions, Needs Clarification, or Not Eligible — each with the specific reasoning behind it 📋 Document Checklist Auto-generated list of what's required to submit, mandatory vs. optional 🔍 Clause-Level Citations Every extracted fact links back to the exact page and verbatim snippet it came from — nothing is taken on faith |
🧪 What-If Simulator Test how a different turnover, experience, or certification profile would change the verdict, without touching the saved result 📥 Downloadable PDF Report A clean, branded eligibility report for internal review or sharing ⚡ Real-Time Processing View Watch the AI work through the document stage by stage, with live progress and page counts 📱 Mobile-Responsive Fully usable from a phone, not just a desktop review tool 🗂️ Case History & Comparison Track every tender evaluated and compare multiple opportunities side by side |
| Layer | Technology |
|---|---|
| Frontend | React 19 + Vite · React Router · Custom CSS design system |
| Backend | Node.js + Express |
| Database | PostgreSQL (Supabase) with Row-Level Security |
| AI / LLM | Google Gemini API |
| PDF Processing | pdf-parse (extraction) · PDFKit (report generation) |
| Auth | JWT-based authentication |
| Hosting | Vercel (frontend) · Render (backend) · Supabase (database) |
🔐 Architecture note: the backend runs three distinct, narrowly-scoped database roles — one for the application itself, one for pre-authentication (signup/login), and one for the background extraction worker — so that Row-Level Security is enforced everywhere it should be, with no single connection able to see every user's data.
TenderIQ's visual language is built around a simple idea: a tender clerk physically stamps a file after days of manual review. TenderIQ does the same thing — instantly.
- Verdicts render as ink stamps, not generic status badges
- Source citations look like case-file page references
- The processing screen reads like a real document being worked through line by line — because it is